/*
 *   This program is free software: you can redistribute it and/or modify
 *   it under the terms of the GNU General Public License as published by
 *   the Free Software Foundation, either version 3 of the License, or
 *   (at your option) any later version.
 *
 *   This program is distributed in the hope that it will be useful,
 *   but WITHOUT ANY WARRANTY; without even the implied warranty of
 *   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *   GNU General Public License for more details.
 *
 *   You should have received a copy of the GNU General Public License
 *   along with this program.  If not, see <http://www.gnu.org/licenses/>.
 */

/*
 *    Normalize.java
 *    Copyright (C) 1999-2012 University of Waikato, Hamilton, New Zealand
 *
 */

package weka.filters.unsupervised.attribute;

import java.util.Collections;
import java.util.Enumeration;
import java.util.Vector;

import weka.core.Capabilities;
import weka.core.Capabilities.Capability;
import weka.core.DenseInstance;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.Option;
import weka.core.OptionHandler;
import weka.core.SparseInstance;
import weka.core.Utils;
import weka.core.WeightedAttributesHandler;
import weka.core.WeightedInstancesHandler;
import weka.filters.Sourcable;
import weka.filters.UnsupervisedFilter;

/**
 * <!-- globalinfo-start --> Normalizes all numeric values in the given dataset
 * (apart from the class attribute, if set). By default, the resulting values
 * are in [0,1] for the data used to compute the normalization intervals. But
 * with the scale and translation parameters one can change that, e.g., with
 * scale = 2.0 and translation = -1.0 you get values in the range [-1,+1].
 * <p/>
 * <!-- globalinfo-end -->
 * 
 * <!-- options-start --> Valid options are:
 * <p/>
 * 
 * <pre>
 * -unset-class-temporarily
 *  Unsets the class index temporarily before the filter is
 *  applied to the data.
 *  (default: no)
 * </pre>
 * 
 * <pre>
 * -S &lt;num&gt;
 *  The scaling factor for the output range.
 *  (default: 1.0)
 * </pre>
 * 
 * <pre>
 * -T &lt;num&gt;
 *  The translation of the output range.
 *  (default: 0.0)
 * </pre>
 * 
 * <!-- options-end -->
 * 
 * @author Eibe Frank (eibe@cs.waikato.ac.nz)
 * @author FracPete (fracpete at waikato dot ac dot nz)
 * @version $Revision$
 */
public class Normalize extends PotentialClassIgnorer implements UnsupervisedFilter, Sourcable, OptionHandler, WeightedInstancesHandler, WeightedAttributesHandler {

    /** for serialization. */
    static final long serialVersionUID = -8158531150984362898L;

    /** The minimum values for numeric attributes. */
    protected double[] m_MinArray;

    /** The maximum values for numeric attributes. */
    protected double[] m_MaxArray;

    /** The translation of the output range. */
    protected double m_Translation = 0;

    /** The scaling factor of the output range. */
    protected double m_Scale = 1.0;

    /**
     * Returns a string describing this filter.
     * 
     * @return a description of the filter suitable for displaying in the
     *         explorer/experimenter gui
     */
    public String globalInfo() {
        return "Normalizes all numeric values in the given dataset (apart from the " + "class attribute, if set). By default, the resulting values are " + "in [0,1] for the data used to compute the normalization intervals. " + "But with the scale and translation parameters one can change that, " + "e.g., with scale = 2.0 and translation = -1.0 you get values in the " + "range [-1,+1].";
    }

    /**
     * Returns an enumeration describing the available options.
     * 
     * @return an enumeration of all the available options.
     */
    @Override
    public Enumeration<Option> listOptions() {

        Vector<Option> result = new Vector<Option>();

        result.addElement(new Option("\tThe scaling factor for the output range.\n" + "\t(default: 1.0)", "S", 1, "-S <num>"));

        result.addElement(new Option("\tThe translation of the output range.\n" + "\t(default: 0.0)", "T", 1, "-T <num>"));

        result.addAll(Collections.list(super.listOptions()));

        return result.elements();
    }

    /**
     * Parses a given list of options.
     * <p/>
     * 
     * <!-- options-start --> Valid options are:
     * <p/>
     * 
     * <pre>
     * -unset-class-temporarily
     *  Unsets the class index temporarily before the filter is
     *  applied to the data.
     *  (default: no)
     * </pre>
     * 
     * <pre>
     * -S &lt;num&gt;
     *  The scaling factor for the output range.
     *  (default: 1.0)
     * </pre>
     * 
     * <pre>
     * -T &lt;num&gt;
     *  The translation of the output range.
     *  (default: 0.0)
     * </pre>
     * 
     * <!-- options-end -->
     * 
     * @param options the list of options as an array of strings
     * @throws Exception if an option is not supported
     */
    @Override
    public void setOptions(String[] options) throws Exception {

        String tmpStr = Utils.getOption('S', options);
        if (tmpStr.length() != 0) {
            setScale(Double.parseDouble(tmpStr));
        } else {
            setScale(1.0);
        }

        tmpStr = Utils.getOption('T', options);
        if (tmpStr.length() != 0) {
            setTranslation(Double.parseDouble(tmpStr));
        } else {
            setTranslation(0.0);
        }

        if (getInputFormat() != null) {
            setInputFormat(getInputFormat());
        }

        super.setOptions(options);

        Utils.checkForRemainingOptions(options);
    }

    /**
     * Gets the current settings of the filter.
     * 
     * @return an array of strings suitable for passing to setOptions
     */
    @Override
    public String[] getOptions() {

        Vector<String> result = new Vector<String>();

        result.add("-S");
        result.add("" + getScale());

        result.add("-T");
        result.add("" + getTranslation());

        Collections.addAll(result, super.getOptions());

        return result.toArray(new String[result.size()]);
    }

    /**
     * Returns the Capabilities of this filter.
     * 
     * @return the capabilities of this object
     * @see Capabilities
     */
    @Override
    public Capabilities getCapabilities() {
        Capabilities result = super.getCapabilities();
        result.disableAll();

        // attributes
        result.enableAllAttributes();
        result.enable(Capability.MISSING_VALUES);

        // class
        result.enableAllClasses();
        result.enable(Capability.MISSING_CLASS_VALUES);
        result.enable(Capability.NO_CLASS);

        return result;
    }

    /**
     * Sets the format of the input instances.
     * 
     * @param instanceInfo an Instances object containing the input instance
     *                     structure (any instances contained in the object are
     *                     ignored - only the structure is required).
     * @return true if the outputFormat may be collected immediately
     * @throws Exception if the input format can't be set successfully
     */
    @Override
    public boolean setInputFormat(Instances instanceInfo) throws Exception {

        super.setInputFormat(instanceInfo);
        setOutputFormat(instanceInfo);
        m_MinArray = m_MaxArray = null;
        return true;
    }

    /**
     * Input an instance for filtering. Filter requires all training instances be
     * read before producing output.
     * 
     * @param instance the input instance
     * @return true if the filtered instance may now be collected with output().
     * @throws Exception             if an error occurs
     * @throws IllegalStateException if no input format has been set.
     */
    @Override
    public boolean input(Instance instance) throws Exception {
        if (getInputFormat() == null) {
            throw new IllegalStateException("No input instance format defined");
        }

        if (m_NewBatch) {
            resetQueue();
            m_NewBatch = false;
        }
        if (m_MinArray == null) {
            bufferInput(instance);
            return false;
        } else {
            convertInstance(instance);
            return true;
        }
    }

    /**
     * Signify that this batch of input to the filter is finished. If the filter
     * requires all instances prior to filtering, output() may now be called to
     * retrieve the filtered instances.
     * 
     * @return true if there are instances pending output
     * @throws Exception             if an error occurs
     * @throws IllegalStateException if no input structure has been defined
     */
    @Override
    public boolean batchFinished() throws Exception {
        if (getInputFormat() == null) {
            throw new IllegalStateException("No input instance format defined");
        }

        if (m_MinArray == null) {
            Instances input = getInputFormat();
            // Compute minimums and maximums
            m_MinArray = new double[input.numAttributes()];
            m_MaxArray = new double[input.numAttributes()];
            for (int i = 0; i < input.numAttributes(); i++) {
                m_MinArray[i] = Double.NaN;
            }

            for (int j = 0; j < input.numInstances(); j++) {
                double[] value = input.instance(j).toDoubleArray();
                for (int i = 0; i < input.numAttributes(); i++) {
                    if (input.attribute(i).isNumeric() && (input.classIndex() != i)) {
                        if (!Utils.isMissingValue(value[i])) {
                            if (Double.isNaN(m_MinArray[i])) {
                                m_MinArray[i] = m_MaxArray[i] = value[i];
                            } else {
                                if (value[i] < m_MinArray[i]) {
                                    m_MinArray[i] = value[i];
                                }
                                if (value[i] > m_MaxArray[i]) {
                                    m_MaxArray[i] = value[i];
                                }
                            }
                        }
                    }
                }
            }

            // Convert pending input instances
            for (int i = 0; i < input.numInstances(); i++) {
                convertInstance(input.instance(i));
            }
        }
        // Free memory
        flushInput();

        m_NewBatch = true;
        return (numPendingOutput() != 0);
    }

    /**
     * Convert a single instance over. The converted instance is added to the end of
     * the output queue.
     * 
     * @param instance the instance to convert
     * @throws Exception if conversion fails
     */
    protected void convertInstance(Instance instance) throws Exception {
        Instance inst = null;
        if (instance instanceof SparseInstance) {
            double[] newVals = new double[instance.numAttributes()];
            int[] newIndices = new int[instance.numAttributes()];
            double[] vals = instance.toDoubleArray();
            int ind = 0;
            for (int j = 0; j < instance.numAttributes(); j++) {
                double value;
                if (instance.attribute(j).isNumeric() && (!Utils.isMissingValue(vals[j])) && (getInputFormat().classIndex() != j)) {
                    if (Double.isNaN(m_MinArray[j]) || (m_MaxArray[j] == m_MinArray[j])) {
                        value = 0;
                    } else {
                        value = (vals[j] - m_MinArray[j]) / (m_MaxArray[j] - m_MinArray[j]) * m_Scale + m_Translation;
                        if (Double.isNaN(value)) {
                            throw new Exception("A NaN value was generated " + "while normalizing " + instance.attribute(j).name());
                        }
                    }
                    if (value != 0.0) {
                        newVals[ind] = value;
                        newIndices[ind] = j;
                        ind++;
                    }
                } else {
                    value = vals[j];
                    if (value != 0.0) {
                        newVals[ind] = value;
                        newIndices[ind] = j;
                        ind++;
                    }
                }
            }
            double[] tempVals = new double[ind];
            int[] tempInd = new int[ind];
            System.arraycopy(newVals, 0, tempVals, 0, ind);
            System.arraycopy(newIndices, 0, tempInd, 0, ind);
            inst = new SparseInstance(instance.weight(), tempVals, tempInd, instance.numAttributes());
        } else {
            double[] vals = instance.toDoubleArray();
            for (int j = 0; j < getInputFormat().numAttributes(); j++) {
                if (instance.attribute(j).isNumeric() && (!Utils.isMissingValue(vals[j])) && (getInputFormat().classIndex() != j)) {
                    if (Double.isNaN(m_MinArray[j]) || (m_MaxArray[j] == m_MinArray[j])) {
                        vals[j] = 0;
                    } else {
                        vals[j] = (vals[j] - m_MinArray[j]) / (m_MaxArray[j] - m_MinArray[j]) * m_Scale + m_Translation;
                        if (Double.isNaN(vals[j])) {
                            throw new Exception("A NaN value was generated " + "while normalizing " + instance.attribute(j).name());
                        }
                    }
                }
            }
            inst = new DenseInstance(instance.weight(), vals);
        }
        inst.setDataset(instance.dataset());
        push(inst, false); // No need to copy
    }

    /**
     * Returns a string that describes the filter as source. The filter will be
     * contained in a class with the given name (there may be auxiliary classes),
     * and will contain two methods with these signatures:
     * 
     * <pre>
     * <code>
     * // converts one row
     * public static Object[] filter(Object[] i);
     * // converts a full dataset (first dimension is row index)
     * public static Object[][] filter(Object[][] i);
     * </code>
     * </pre>
     * 
     * where the array <code>i</code> contains elements that are either Double,
     * String, with missing values represented as null. The generated code is public
     * domain and comes with no warranty.
     * 
     * @param className the name that should be given to the source class.
     * @param data      the dataset used for initializing the filter
     * @return the object source described by a string
     * @throws Exception if the source can't be computed
     */
    @Override
    public String toSource(String className, Instances data) throws Exception {
        StringBuffer result;
        boolean[] process;
        int i;

        result = new StringBuffer();

        // determine what attributes were processed
        process = new boolean[data.numAttributes()];
        for (i = 0; i < data.numAttributes(); i++) {
            process[i] = (data.attribute(i).isNumeric() && (i != data.classIndex()));
        }

        result.append("class " + className + " {\n");
        result.append("\n");
        result.append("  /** lists which attributes will be processed */\n");
        result.append("  protected final static boolean[] PROCESS = new boolean[]{" + Utils.arrayToString(process) + "};\n");
        result.append("\n");
        result.append("  /** the minimum values for numeric values */\n");
        result.append("  protected final static double[] MIN = new double[]{" + Utils.arrayToString(m_MinArray).replaceAll("NaN", "Double.NaN") + "};\n");
        result.append("\n");
        result.append("  /** the maximum values for numeric values */\n");
        result.append("  protected final static double[] MAX = new double[]{" + Utils.arrayToString(m_MaxArray) + "};\n");
        result.append("\n");
        result.append("  /** the scale factor */\n");
        result.append("  protected final static double SCALE = " + m_Scale + ";\n");
        result.append("\n");
        result.append("  /** the translation */\n");
        result.append("  protected final static double TRANSLATION = " + m_Translation + ";\n");
        result.append("\n");
        result.append("  /**\n");
        result.append("   * filters a single row\n");
        result.append("   * \n");
        result.append("   * @param i the row to process\n");
        result.append("   * @return the processed row\n");
        result.append("   */\n");
        result.append("  public static Object[] filter(Object[] i) {\n");
        result.append("    Object[] result;\n");
        result.append("\n");
        result.append("    result = new Object[i.length];\n");
        result.append("    for (int n = 0; n < i.length; n++) {\n");
        result.append("      if (PROCESS[n] && (i[n] != null)) {\n");
        result.append("        if (Double.isNaN(MIN[n]) || (MIN[n] == MAX[n]))\n");
        result.append("          result[n] = 0;\n");
        result.append("        else\n");
        result.append("          result[n] = (((Double) i[n]) - MIN[n]) / (MAX[n] - MIN[n]) * SCALE + TRANSLATION;\n");
        result.append("      }\n");
        result.append("      else {\n");
        result.append("        result[n] = i[n];\n");
        result.append("      }\n");
        result.append("    }\n");
        result.append("\n");
        result.append("    return result;\n");
        result.append("  }\n");
        result.append("\n");
        result.append("  /**\n");
        result.append("   * filters multiple rows\n");
        result.append("   * \n");
        result.append("   * @param i the rows to process\n");
        result.append("   * @return the processed rows\n");
        result.append("   */\n");
        result.append("  public static Object[][] filter(Object[][] i) {\n");
        result.append("    Object[][] result;\n");
        result.append("\n");
        result.append("    result = new Object[i.length][];\n");
        result.append("    for (int n = 0; n < i.length; n++) {\n");
        result.append("      result[n] = filter(i[n]);\n");
        result.append("    }\n");
        result.append("\n");
        result.append("    return result;\n");
        result.append("  }\n");
        result.append("}\n");

        return result.toString();
    }

    /**
     * Returns the calculated minimum values for the attributes in the data.
     * 
     * @return the array with the minimum values
     */
    public double[] getMinArray() {
        return m_MinArray;
    }

    /**
     * Returns the calculated maximum values for the attributes in the data.
     * 
     * @return the array with the maximum values
     */
    public double[] getMaxArray() {
        return m_MaxArray;
    }

    /**
     * Returns the tip text for this property.
     * 
     * @return tip text for this property suitable for displaying in the
     *         explorer/experimenter gui
     */
    public String scaleTipText() {
        return "The factor for scaling the output range (default: 1).";
    }

    /**
     * Get the scaling factor.
     * 
     * @return the factor
     */
    public double getScale() {
        return m_Scale;
    }

    /**
     * Sets the scaling factor.
     * 
     * @param value the scaling factor
     */
    public void setScale(double value) {
        m_Scale = value;
    }

    /**
     * Returns the tip text for this property.
     * 
     * @return tip text for this property suitable for displaying in the
     *         explorer/experimenter gui
     */
    public String translationTipText() {
        return "The translation of the output range (default: 0).";
    }

    /**
     * Get the translation.
     * 
     * @return the translation
     */
    public double getTranslation() {
        return m_Translation;
    }

    /**
     * Sets the translation.
     * 
     * @param value the translation
     */
    public void setTranslation(double value) {
        m_Translation = value;
    }

    /**
     * Main method for running this filter.
     * 
     * @param args should contain arguments to the filter, use -h for help
     */
    public static void main(String[] args) {
        runFilter(new Normalize(), args);
    }
}
